Data-Driven Decision Architectures in Healthcare: The Role of AI in Enhancing Predictive Capabilities

Breast cancer affects women all around the world, and it can be dangerous. As stated by clinical specialists, early detection of cancer saves deaths. Lot of the machine-learning algorithms have been created to categorize tumors to identify breast cancer, and medical imagery processing is a useful tool for improving classification accuracy. Micro classifications are among the most important indications for early detection of breast cancer. Many investigations have used machine learning methods to address this issue. Numerous classification methods are employed to detect the possibility of breast cancer, either benign or malignant. An image-enhancing technique has been designed to aid in the earlier identification and diagnosis of cancer. The present research attempts to categorize breast cancer using a machine-learning method. The goal of the current study was to use machine learning (ML) techniques to increase the reliability of disease diagnosis. This study assesses and chooses a number of breast cancer prediction methods based on their accuracy, interpretability, and ease of use.